In recent decades, green aspects became a key priority for governments worldwide, as sustainable policies are able to promote a more equitable society and a healthier economy from the social, economic, and environmental perspectives, in addition to preserving natural resources for future generations. As an essential context in information technology management, green information technology (GIT) has been developed to cope with the existing environmental problems through organizations. The present study is aimed at identifying the influential factors of decision-making on the adoption of GIT. To collect the required data, interviews were performed along with a structured survey. A total of 112 questionnaires were delivered to chief information officers (CIOs), 99 of which underwent the analysis. The structural equation and partial least square approaches were adopted for data analysis. GIT driver (G-driver) was found to be an intermediary parameter. Findings revealed the GIT readiness (G-readiness) and GIT context (G-context) result in in GIT adoption whenever there was a G-driver indicator (i.e., ethical driver, economic driver, response driver, or regulatory driver). The present study found the significance of all the variables to be above 1.96 except G-context −> green intention to adoption path and G-readiness −> green intention to adoption. Considering that the determination of coefficients and the analysis of relationships between factors directly depends on the opinion of experts and if the opinion of expert’s changes, the results will also change; this can be mentioned as the most important limitation of the research. Therefore, it should be noted that the largest impact was identified to be posed by the economical driver.

In this research, a novel mathematical model for sustainable supply chain network design is proposed. The main contribution and novelty of this research are considering environmentally friendly facilities and several thresholds for emitted pollution, which bring this research closer to real-world conditions. Since the amount of pollution produced by different supply chain facilities is not the same, it is better to make different decisions regarding each repair and renovation measure, i.e., environmental decisions should be considered step-by-step. In addition, the proposed model was optimized by the whale optimization algorithm (WOA) to find the best solution in large-scale instances in a short and reasonable time. Finally, the performance of the proposed model and the reduction of environmental impacts to improve the stability of logistics systems are reviewed in a case study. The results of the computational analysis show the efficiency of the proposed model.